Identifying online risk markers of hard-to-observe crimes through semi-inductive triangulation: The case of human trafficking in the United States

Ieke de Vries and Jason Radford

The British Journal of Criminology, 2022, vol. 62, issue 3, 639-658

Abstract: Many types of crime are difficult to study because they are hard to operationalize, hidden from the public, or both. With communication increasingly moving to online domains, recent work has begun to examine whether the online domain contains traces of such hard-to-observe crimes. This study explores the online linguistic contours of hard-to-observe crimes through a rigorous mixed-methods approach that combines interviews and computational text analysis. Using human trafficking in illicit massage businesses as a proof-of-concept, we show how this approach, which we call semi-inductive triangulation, meets the empirical contextuality and relationality of crime traces in the online domain. The findings contribute to an emerging field of computational criminology and call for an integration of linguistic approaches in criminology.

Keywords: computational criminology; computational methods; mixed methods; online data; human trafficking (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:oup:crimin:v:62:y:2022:i:3:p:639-658.

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